Drivers of COVID-19 variant wave dynamics: inferring oncoming wave size using global data with genomics
Bibliographic record
Abstract
The continued evolution of the SARS-CoV-2 virus drove waves of infection worldwide throughout the pandemic. These evolutionary dynamics posed significant challenges for public health forecasting and, specifically, for predicting the size of COVID-19 waves. In this work we leverage a range of global public data, with a focus on features derived from pathogen genomic sequences, to model and predict the relative size of COVID-19 waves (as compared to the previous wave) across countries. Focusing on Omicron BA.1 and BA.2, we develop statistical models to assess the predictive power of these data in forecasting future variant-driven wave peaks. We find that, while forecasting wave size is a challenging task, variables such as genomic variant characteristics, prior wave dynamics, and demographic features e.g. life expectancy were informative, whereas seasonality was not. Our results show that the importance of features changed markedly between Omicron waves, reflecting the evolving epidemiological and genomic landscape. This work provides insights into improving predictive models for future outbreaks and pandemics, and prioritizing data collection efforts to enhance forecasting accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".